NYC Traffic Crash Trends: A Post-Covid Analysis

Introduction
In our project, we take a look into the impact of the COVID-19 lockdowns on traffic collisions in New York City. The project is centered around the idea of exploring how these unprecedented lockdowns, which drastically changed urban lifestyles, particularly in one of the world's most congested cities, influenced road safety and traffic patterns. This exploration is crucial for understanding the pandemic's broader effects on urban mobility.
Data set
The dataset for our project encompasses comprehensive information on motor vehicle collisions in New York City, spanning from 2012 to 2023. This data, which is derived from all police-reported motor vehicle collisions in the city, is meticulously compiled in the Motor Vehicle Collisions crash table. Each row in this table details a specific crash event, offering a granular view of the circumstances and outcomes of these incidents. The dataset's foundation is the police report MV-104-AN, which is mandated for collisions involving injury, death, or property damage exceeding $1000. It's important to note that this dataset is preliminary and may be subject to revisions, as the MV-104AN forms can be amended with updated crash details.
Methology
We implemented a structured approach to process and analyze the New York City motor vehicle collision data. Our first step was to filter the dataset from the years 2017 to 2022. This range was chosen to focus on the most recent complete years, covering the period immediately before and during the COVID-19 pandemic, to capture the most relevant changes in traffic patterns.
To ensure the quality and reliability of our analysis, we undertook a thorough data cleaning process. This involved removing variables that were missing 80% or more of their values, to maintain the integrity of our dataset. Additionally, we eliminated duplicate values to avoid skewing our results with redundant data. Furthermore, we created a standardized date variable. This standardization was necessary to facilitate a consistent and accurate temporal analysis of the data. By having a uniform date format, we were able to conduct more precise comparisons and trend analyses over the selected time period.
Visulisations
Time-Series: Using time-series graphs, we can see that the number of crashes went down, immediately after Covid-19 was declared a pandemic. Although the number of collisions increased again after 2020, they are still significantly below pre-Covid number.
Heat Maps: By mapping collision hotspots, we tried to identify shifts in high-incident areas pre and post-lockdown, indicating changes in traffic patterns and potentially altered commuting habits.
Comparative Analysis: We use bar charts and stacked area charts to compare different variables year-over-year, to highlight any variations in collision types and severity, with an increase in a certain collision type post lockdown.
Conclusion
Our analysis revealed a nuanced landscape of traffic collisions in NYC's post-COVID era, showing a decrease in incidents yet an uptick in driver fatalities. This complexity signals a need for further investigation into the intricate relationship between pandemic policies and road safety, informing future urban planning and safety measures.